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Adversarial Learning of Knowledge Embeddings for the Unified Medical Language System
Ramon Maldonado1, Meliha Yetisgen2, Sanda M Harabagiu1
1University of Texas at Dallas, Richardson, TX, USA.
Abstract:
Incorporating the knowledge encoded in the Unified Medical Language System (UMLS) in deep learning methods requires learning knowledge embeddings from the knowledge graphs available in UMLS: the Metathesaurus and the Semantic Network. In this paper we present a technique using Generative Adversarial Networks (GANs) for learning UMLS embeddings and showcase their usage in a clinical prediction model. When the UMLS embeddings are available, the predictions improve by up to 6.9% absolute F1 score.
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